Summary

In this chapter, we took a second stab at the unsupervised learning techniques by exploring PCA, examining what it is, and applying it in a practical fashion. We explored how it can be used to reduce the dimensionality and improve the understanding of the dataset, especially when confronted with numerous highly correlated variables. Then, we applied it to a real and current dataset from the National Hockey League, using the resulting principal components in a regression analysis and exploring ways to visualize the data. As an unsupervised learning technique, it requires some judgment along with trial and error to arrive at an optimal solution. We will next look at using unsupervised learning to develop market basket analyses and recommendation engines in which PCA can play an important role.